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Quantifying Qualitative Data for Understanding Controversial Issues.

, , , and . LREC, European Language Resources Association (ELRA), (2018)

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Comparing Target Sets for Stance Detection: A Case Study on YouTube Comments on Death Penalty., and . KONVENS, page 69-79. Österreichische Akademie der Wissenschaften, (2018)ltl.uni-due at SemEval-2019 Task 5: Simple but Effective Lexico-Semantic Features for Detecting Hate Speech in Twitter., , , and . SemEval@NAACL-HLT, page 441-446. Association for Computational Linguistics, (2019)Measuring the Reliability of Hate Speech Annotations: The Case of the European Refugee Crisis., , , , , and . CoRR, (2017)Task-Independent Features for Automated Essay Grading., , and . BEA@NAACL-HLT, page 224-232. The Association for Computer Linguistics, (2015)Neural, Non-neural and Hybrid Stance Detection in Tweets on Catalan Independence., and . IberEval@SEPLN, volume 1881 of CEUR Workshop Proceedings, page 178-184. CEUR-WS.org, (2017)LTL-UDE at SemEval-2019 Task 6: BERT and Two-Vote Classification for Categorizing Offensiveness., , , and . SemEval@NAACL-HLT, page 678-682. Association for Computational Linguistics, (2019)Bundled Gap Filling: A New Paradigm for Unambiguous Cloze Exercises., , and . BEA@NAACL-HLT, page 172-181. The Association for Computer Linguistics, (2016)Quantifying Qualitative Data for Understanding Controversial Issues., , , and . LREC, European Language Resources Association (ELRA), (2018)Do Women Perceive Hate Differently: Examining the Relationship Between Hate Speech, Gender, and Agreement Judgments., , , and . KONVENS, page 110-120. Österreichische Akademie der Wissenschaften, (2018)Haptic 3D Surface Representation of Table-Based Data for People With Visual Impairments., , , , , and . TACCESS, 6 (1): 1:1-1:35 (2015)